Tampering Detection and Localization in Images from Social Networks: A CBIR Approach

  • Cedric MaigrotEmail author
  • Ewa Kijak
  • Ronan Sicre
  • Vincent Claveau
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10484)


Verifying the authenticity of an image on social networks is crucial to limit the dissemination of false information. In this paper, we propose a system that provides information about tampering localization on such images, in order to help either the user or automatic methods to discriminate truth from falsehood. These images may be subjected to a large number of possible forgeries, which calls for the use of generic methods. Image forensics methods based on local features proved to be effective for the specific case of copy-move forgery. By taking advantage of the number of images available on the internet, we propose a generic system based on image retrieval, followed by image comparison based on local features to localize any kind of tampering in images from social networks. We also propose a large and challenging adapted database of real case images for evaluation.


Tampering detection and localization Tweet image analysis Image forgery Copy-move and splicing detection Matching 


  1. 1.
    Amerini, I., Ballan, L., Caldelli, R., Bimbo, A.D., Tongo, L.D., Serra, G.: Copy-move forgery detection and localization by means of robust clustering with J-linkage. Signal Process.: Image Commun. (SPIC) 28, 659–669 (2013)Google Scholar
  2. 2.
    Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Serra, G.: A SIFT-based forensic method for copy-move attack detection and transformation recovery. TIFS 6, 1099–1110 (2011)Google Scholar
  3. 3.
    Babenko, A., Lempitsky, V.S.: Aggregating local deep features for image retrieval. In: International Conference on Computer Vision (ICCV) (2015)Google Scholar
  4. 4.
    Babenko, A., Slesarev, A., Chigorin, A., Lempitsky, V.: Neural codes for image retrieval. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 584–599. Springer, Cham (2014). doi: 10.1007/978-3-319-10590-1_38 Google Scholar
  5. 5.
    Bayar, B., Stamm, M.C.: A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Workshop on IHMS (2016)Google Scholar
  6. 6.
    Christlein, V., Riess, C., Angelopoulou, E.: On rotation invariance in copy-move forgery detection. In: Workshop on Information Forensics and Security (2010)Google Scholar
  7. 7.
    Christlein, V., Riess, C., Jordan, J., Riess, C., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. Trans. Inf. Forensics Secur. (TIFS) 7, 1841–1854 (2012)CrossRefGoogle Scholar
  8. 8.
    Cozzolino, D., Poggi, G., Verdoliva, L.: Efficient dense-field copy–move forgery detection. Trans. Info. Forensics Secur. (TIFS) 10, 2284–2297 (2015)CrossRefGoogle Scholar
  9. 9.
    Dixit, A., Dixit, R., Gupta, R.K.: Detection of copy-move forgery exploiting LBP features with discrete wavelet transform. Int. J. Comput. Appl. (3), 1–10 (2016)Google Scholar
  10. 10.
    Fan, Y., Zhu, Y.S., Liu, Z.: An improved sift-based copy-move forgery detection method using T-linkage and multi-scale analysis. IHMSP 7, 399–408 (2016)Google Scholar
  11. 11.
    Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. Trans. Inf. Forensics Secur. (TIFS) 7, 868–882 (2012)CrossRefGoogle Scholar
  12. 12.
    Jegou, H., Douze, M., Schmid, C.: Hamming embedding and weak geometric consistency for large scale image search. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 304–317. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88682-2_24 CrossRefGoogle Scholar
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Neural Information Processing Systems 25 (2012)Google Scholar
  14. 14.
    Li, Y., Zhou, J.: Image copy-move forgery detection using hierarchical feature point matching. In: APSIPA Transactions on Signal and Information Processing (2016)Google Scholar
  15. 15.
    Maigrot, C., Claveau, V., Kijak, E., Sicre, R.: MediaEval 2016: a multimodal system for the verifying multimedia use task. In: MediaEval Workshop (2016)Google Scholar
  16. 16.
    Phan, Q.T., Budroni, A., Pasquini, C., De Natale, F.G.: A hybrid approach for multimedia use verification. In: MediaEval Workshop (2016)Google Scholar
  17. 17.
    Rao, Y., Ni, J.: A deep learning approach to detection of splicing and copy-move forgeries in images. In: Workshop on Information Forensics and Security (2016)Google Scholar
  18. 18.
    Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: CVPR Workshops (2014)Google Scholar
  19. 19.
    Sicre, R., Gevers, T.: Dense sampling of features for image retrieval. In: ICIP, pp. 3057–3061. IEEE (2014)Google Scholar
  20. 20.
    Sicre, R., Jégou, H.: Memory vectors for particular object retrieval with multiple queries. In: ICMR. ACM (2015)Google Scholar
  21. 21.
    Sicre, R., Jurie, F.: Discriminative part model for visual recognition. CVIU 141, 28–37 (2015)Google Scholar
  22. 22.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2014)Google Scholar
  23. 23.
    Tolias, G., Sicre, R., Jégou, H.: Particular object retrieval with integral max-pooling of CNN activations. In: ICLR (2016)Google Scholar
  24. 24.
    Warbhe, A.D., Dharaskar, R., Thakare, V.: A survey on keypoint based copy-paste forgery detection techniques. Proced. Comput. Sci. 78, 61–67 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Cedric Maigrot
    • 1
    Email author
  • Ewa Kijak
    • 1
  • Ronan Sicre
    • 2
  • Vincent Claveau
    • 2
  1. 1.Univ. Rennes I, UMR 6074 IRISARennesFrance
  2. 2.CNRS, UMR 6074 IRISARennesFrance

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